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000890309 1001_ $$0P:(DE-Juel1)143792$$aGalldiks, Norbert$$b0$$eCorresponding author$$ufzj
000890309 245__ $$aArtificial Intelligence, Radiomics, and Deep Learning in Neuro-Oncology
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000890309 520__ $$aBesides the histomolecular evaluation of tissue samples obtained from resection or biopsy, neuroimaging forms the basis for the diagnosis of brain cancer. Contrast-enhanced MRI is the method of choice for brain tumor diagnostics, treatment planning, and follow-up. Currently, advanced MRI techniques as well as amino acid PET are increasingly applied, generating a large variety of imaging parameters for brain tumor diagnostics. This is also driven by the increasing availability of hybrid PET/CT and PET/MRI scanners. Evaluation of the complex, multiparametric imaging data can be achieved by methods from the emerging field of artificial intelligence, potentially supporting physicians in clinical routine. For example, time-consuming steps such as manual detection and segmentation of lesions can be performed fully automatically. Since computer-aided image analysis is independent of the experience level of the evaluating physician, the results are more standardized and improve the inter-institutional comparability.
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000890309 773__ $$0PERI:(DE-600)3009682-0$$a10.1093/noajnl/vdaa179$$gVol. 2, no. Supplement_4, p. iv1 - iv2$$nSupplement_4$$piv1 - iv2$$tNeuro-oncology advances$$v2$$x2632-2498$$y2021
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